Drivers of deep-water renewal events observed over 13 years in the South Basin of Lake Baikal

Lake Baikal, with a depth of 1637 m, is characterized by deep-water intrusions that bridge the near-surface layer to the hypolimnion. These episodic events transfer heat and oxygen over large vertical scales and maintain the permanent temperature stratified deep-water status of the lake. Here we evaluate a series of intrusion events that reached the bottom of the lake in terms of the stratification and the wind conditions under which they occurred and provide a new insight into the triggering mechanisms. We make use of long-term temperature and current meter data (2000-2013) recorded in the South Basin of the lake combined with wind data produced with a regional downscaling of the global NCEP-RA1 reanalysis product. A total of 13 events were observed during which near-surface cold water reached the bottom of the South Basin at 1350 m depth. We found that the triggering mechanism of the events is related to the time of the year that they take place. We categorized the events in three groups: (1) winter events, observed shortly before the complete ice cover of the lake that are triggered by Ekman coastal downwelling, (2) under-ice events, and (3) spring events, that show no correlation to the wind conditions and are possibly connected to the increased spring outflow of the Selenga River

Lake Baikal deepwater renewal mystery solved

Deepwater renewal by intrusions and turbulent diffusion in Lake Baikal is very effective despite the enormous depth of up to 1642 m and the permanently stable stratification below similar to 300 m depth. Temperature time series recorded at the bottom of a mooring installed since March 2000 in the South Basin of the lake indicate recurrent freshwater intrusions with volumes of 50 to 100 km 3, about one order of magnitude larger than previously observed intrusions. Numerous mechanisms have been proposed to explain the advective deep water renewal. Here we present for the first time direct observations which prove that they are caused by coastal downwelling and subsequent thermobaric instability along the steep lake shores. Understanding these mechanisms is an important prerequisite for studying biogeochemical cycles, for predicting the effects of climate change on this unique ecosystem and for evaluating the local climate history from the extraordinary sedimentary record of Lake Baikal

CNN-Based Classifier as an Offline Trigger for the CREDO Experiment

Gamification is known to enhance users’ participation in education and research projects that follow the citizen science paradigm. The Cosmic Ray Extremely Distributed Observatory (CREDO) experiment is designed for the large-scale study of various radiation forms that continuously reach the Earth from space, collectively known as cosmic rays. The CREDO Detector app relies on a network of involved users and is now working worldwide across phones and other CMOS sensor-equipped devices. To broaden the user base and activate current users, CREDO extensively uses the gamification solutions like the periodical Particle Hunters Competition. However, the adverse effect of gamification is that the number of artefacts, i.e., signals unrelated to cosmic ray detection or openly related to cheating, substantially increases. To tag the artefacts appearing in the CREDO database we propose the method based on machine learning. The approach involves training the Convolutional Neural Network (CNN) to recognise the morphological difference between signals and artefacts. As a result we obtain the CNN-based trigger which is able to mimic the signal vs. artefact assignments of human annotators as closely as possible. To enhance the method, the input image signal is adaptively thresholded and then transformed using Daubechies wavelets. In this exploratory study, we use wavelet transforms to amplify distinctive image features. As a result, we obtain a very good recognition ratio of almost 99% for both signal and artefacts. The proposed solution allows eliminating the manual supervision of the competition process

Cosmic-Ray Extremely Distributed Observatory

The Cosmic-Ray Extremely Distributed Observatory (CREDO) is a newly formed, global collaboration dedicated to observing and studying cosmic rays (CR) and cosmic-ray ensembles (CRE): groups of at least two CR with a common primary interaction vertex or the same parent particle. The CREDO program embraces testing known CR and CRE scenarios, and preparing to observe unexpected physics, it is also suitable for multi-messenger and multi-mission applications. Perfectly matched to CREDO capabilities, CRE could be formed both within classical models (e.g., as products of photon–photon interactions), and exotic scenarios (e.g., as results of decay of Super-Heavy Dark Matter particles). Their fronts might be significantly extended in space and time, and they might include cosmic rays of energies spanning the whole cosmic-ray energy spectrum, with a footprint composed of at least two extensive air showers with correlated arrival directions and arrival times. As the CRE are predominantly expected to be spread over large areas and, due to the expected wide energy range of the contributing particles, such a CRE detection might only be feasible when using all available cosmic-ray infrastructure collectively, i.e., as a globally extended network of detectors. Thus, with this review article, the CREDO Collaboration invites the astroparticle physics community to actively join or to contribute to the research dedicated to CRE and, in particular, to pool together cosmic-ray data to support specific CRE detection strategies